In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization （PSO） with differential evoluti...In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization （PSO） with differential evolution （DE） and a new chaotic local search. In the CDEPSO algorithm, DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO, while the best solution in the PSO is performed by chaotic local search. To investigate the performance of CDEPSO, four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO （a hybrid algorithm which only combines PSO with chaotic local search）. And, compared with the other six improved meta-heuristics, CDEPSO also exhibits more robust performance. In addition, a new performance was proposed to more fairly compare CDEPSO with the same six improved recta-heuristics, and CDEPSO algorithm is the best in solving these problems.展开更多
A modified artificial bee colony optimizer(MABC)is proposed for image segmentation by using a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff.The main idea of MABC is to enrich...A modified artificial bee colony optimizer(MABC)is proposed for image segmentation by using a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff.The main idea of MABC is to enrichartificial bee foraging behaviors by combining local search and comprehensive learning using multi-dimensional PSO-based equation.With comprehensive learning,the bees incorporate the information of global best solution into the solution search equation to improve the exploration while the local search enables the bees deeply exploit around the promising area,which provides a proper balance between exploration and exploitation.The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm.Furthermore,we applied the MABC algorithm to image segmentation problem.Experimental results verify the effectiveness of the proposed algorithm.展开更多
基金Project(20040533035)supported by the National Research Foundation for the Doctoral Program of Higher Education of ChinaProject(60874070)supported by the National Natural Science Foundation of China
文摘In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization （PSO） with differential evolution （DE） and a new chaotic local search. In the CDEPSO algorithm, DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO, while the best solution in the PSO is performed by chaotic local search. To investigate the performance of CDEPSO, four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO （a hybrid algorithm which only combines PSO with chaotic local search）. And, compared with the other six improved meta-heuristics, CDEPSO also exhibits more robust performance. In addition, a new performance was proposed to more fairly compare CDEPSO with the same six improved recta-heuristics, and CDEPSO algorithm is the best in solving these problems.
基金Projects(6177021519,61503373)supported by National Natural Science Foundation of ChinaProject(N161705001)supported by Fundamental Research Funds for the Central University,China
文摘A modified artificial bee colony optimizer(MABC)is proposed for image segmentation by using a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff.The main idea of MABC is to enrichartificial bee foraging behaviors by combining local search and comprehensive learning using multi-dimensional PSO-based equation.With comprehensive learning,the bees incorporate the information of global best solution into the solution search equation to improve the exploration while the local search enables the bees deeply exploit around the promising area,which provides a proper balance between exploration and exploitation.The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm.Furthermore,we applied the MABC algorithm to image segmentation problem.Experimental results verify the effectiveness of the proposed algorithm.
